CS 638 Fall 2016: Introduction to Data Science

TR 2:30-3:45pm in 1221 CS Building, 3 Credits

Announcements

• The class mailing list is compsci638-1-f16@lists.wisc.edu

  • The class's Piazza page is at http://piazza.com/wisc/fall2016/cs638. This is a forum for the students. We will monitor occasionally but do not have enough man power to answer all questions posted to this page.

Instructor & TAs

• AnHai Doan, contact information available from my homepage.

• Office hours: Thursdays 4-5pm and by appointment (pls send email, thanks)

• TAs:

  • Rashi Jalan <rjalan@wisc.edu>, office hours: Mon 11-12 and Wed 4-5, Room 1306.

    • and Sidharth Mudgal <sidharth@cs.wisc.edu>, office hours: Tue 11-12 and Fri 2-3, Room 1304.

Course Description, Prerequisites, and FAQs

Course Format & Grading

• • See the above course description

  • Midterm: Thur Oct 27, in class at usual time/room

  • Final: Mon Dec 19, 7:25pm-9:25pm

  • Other important dates: first class: Tue Sept 6, Thanksgiving break: Nov 24-27, last class: Thur Dec 15

  • Grading: midterm: 30%, final: 30%, project: 40%

Lecture Slides (Tentative)

• Introduction

• Background: tools, data models/formats, and attributes

  • Background: data storage and processing (RDBMSs, NoSQL, Hadoop, etc.)

  • Background: machine learning (a brief introduction)

• Background: cloud computing and crowdsourcing

  • The rise of data science and the current data science landscape

• Data acquisition

• Data extraction

  • • Extraction from template-based data (aka wrapper-based extraction)

  • Information extraction from text

• Data understanding, cleaning, transformation

  • Data integration

  • String matching

  • Data matching

  • Schema matching and putting it all together

  • Data exploration and analysis

  • OLAP

    • classification, clustering

    • association rule mining (see the book chapter)

    • anomaly detection

• Building data-intensive artifacts & designing data-intensive experiments

• Cross-cutting issues

  • • cross-cutting techniques, execution stages, workflow management, team organization

    • the three Ss: stages, steps, stacks

    • scaling, quality monitoring, crowdsourcing, etc.

    • implementation/architectures

Project

• • Students will form teams for a multi-stage project that addresses a data science problem.

  • Project page.

Resources

• • Click here for resources to learn Python, pandas, machine learning, more data science, etc.

• If you want to study more data science at UW

Misc

• What is the hottest job in Chicago tech today?

  • dotdatascience.org UW-madison student organization focused on data science